AI Vision Safety Solution

Q: What does implementation and onboarding look like?

Discovery and camera assessment take about a week, a pilot runs two to four weeks against recorded footage, and full rollout with alert routing and training adds another two to four weeks. Supervisors need roughly an hour of onboarding.

About Braintix

Turn the cameras you already own into a safety system

Braintix Labs builds an AI layer on top of existing CCTV. Instead of recording incidents for someone to find later, the system watches the live stream, recognises the situations you care about, and raises an alert while there is still time to act.

A vision ai safety layer is not a new surveillance network — it is intelligence added to the one you have. Every deployment starts from a specific safety problem on a specific site — a blind corner, a forklift aisle, a gate where hard hats get skipped — and is validated against footage from your own cameras before it goes live.

Our Expertise in AI Vision Technology

We build and deploy computer vision models on live RTSP and IP camera streams: object and person detection, zone breach, PPE recognition, proximity and dwell-time analytics. Our team has taken these systems from proof of concept into daily operation on real sites, where lighting is poor, lenses are dirty and half the cameras were installed a decade ago. Making vision ai safety work under those conditions is the actual engineering problem, and it is the one we solve.

Approach to Workplace Safety

Safety problem first, model second. We agree with your EHS team on the three or four events that matter most on your site, tune detection thresholds against your own recorded footage, and only then connect alerts to real people. We do not promise a detection rate before we have seen your cameras. Where a call is ambiguous, the system escalates to a human rather than deciding on its own.

Built on Your Existing Infrastructure

No rip-and-replace. We connect to the NVR or camera streams you run today, process on the edge or in your own cloud depending on your privacy requirements, and push events into the channels your team already watches — Telegram, email, a dashboard or your existing incident system.

The Gap

Why Cameras Alone Aren’t Enough

A camera records. It does not notice. On most sites the footage is only opened after something has already gone wrong, which makes the whole system a forensic tool rather than a preventive one. That is the gap computer vision for safety closes.

Problems Get Noticed Too Late

Nobody can watch forty screens continuously, and attention on a video wall drops sharply within the first half hour of a shift. A spill, a blocked fire exit or an unauthorised entry into a restricted zone sits unseen until someone happens to walk past — or until it causes an injury. By the time the recording is reviewed, the decision that mattered was hours old.

Missed PPE Violations

Hard hats, high-vis vests, gloves and safety glasses are enforced by spot checks, which means compliance is measured for a few minutes a week and assumed for the rest. Contractors and short-term staff are the hardest group to cover and the most likely to skip a step. Continuous automated checking changes enforcement from an occasional audit into a constant, even standard applied to everyone equally.

Hard to Prove Safety Compliance

When an insurer, a regulator or a client asks for evidence of your safety regime, most operations can produce a policy document and a paper log. What they cannot produce is a time-stamped record of what actually happened on the floor. Without objective data, disputes over liability come down to testimony, and safety investment stays impossible to justify with numbers.

Near-Misses Are Never Recorded

The incident that almost happened is the most valuable safety data you have, and it is almost never captured. A pedestrian stepping clear of a reversing forklift leaves no trace in any log. Automated detection turns those moments into a counted, mapped dataset — so you can fix the aisle layout before the near-miss becomes a claim.

Find out what your cameras are already missing

Send us a few hours of recorded footage. We will run our detection models over it and show you, frame by frame, which events your current setup did not catch.

Book a Consultation

Safety manager reviewing detected hazards on a workplace camera analytics dashboard

Prefer to talk?

+380 73 374 03 08

What We Build

Our AI Vision Safety Solution

Four capabilities, deployed together or one at a time. Each runs on your existing camera feeds and writes every detected event into a searchable log with the video clip attached. A safety vision ai solution is only useful if people act on what it finds, so every alert is built around a clear owner and a clear next step.

Real-Time Hazard Detection

The system watches for the conditions you define: spills and obstructions, blocked emergency exits, people entering restricted or machine-active zones, unattended objects, smoke and fire signatures, and workers in areas they are not cleared for. Detection happens on the live stream, so the alert arrives in seconds rather than after a review. Each alert carries the camera, the timestamp and a short clip, so whoever responds already knows what they are walking into.

PPE Compliance Monitoring

Hard hats, vests, gloves, goggles and footwear are checked continuously at the points that matter — entry gates, production lines, loading bays. Rules are set per zone, because a warehouse aisle and a welding bay do not need the same kit. Violations are logged with evidence and can trigger an immediate local alert, a supervisor notification, or a weekly compliance report. This is where ai computer vision safety produces the clearest, fastest measurable change in behaviour.

Works With Your Existing Cameras

If your cameras output an RTSP or IP stream, we can use them — no new hardware, no re-cabling, no replacing an NVR that works. We assess coverage first and tell you honestly which angles are usable and which are not; sometimes the answer is that one camera needs moving two metres. Processing runs on an edge device on site or in your private cloud, whichever your data policy requires. Using computer vision for safety on hardware you already own is what keeps the cost of entry low enough to pilot properly.

Near-Miss and Proximity Alerts

Person-to-vehicle and person-to-machine distance is tracked continuously, so a forklift passing too close to a pedestrian is counted even when nobody was hurt. Over a few weeks this builds a heat map of where your site is genuinely dangerous, ranked by frequency rather than by whoever complained loudest. That data is what turns an ai vision safety system from a monitoring tool into a planning one.

Delivery Model

How It Works

Deployment runs in four stages, and you can stop after any of them. Nothing is connected to live alerting until it has been validated on footage from your own site — a typical single-site rollout takes four to eight weeks.

Discovery

We review your camera layout, stream formats, network and incident history, then agree with your EHS team on the events to detect first and who receives each alert. Output: a written scope with coverage map, detection list and data-handling rules.

Pilot

Models run on a small group of cameras against your recorded footage. You see true and false detection rates per event type before anything is connected to live alerts, and we tune thresholds until the signal is worth acting on.

Implementation

The validated setup is rolled out across the site: edge or private-cloud processing, zone rules, escalation paths, dashboards, event logging and training for supervisors and the safety team.

Support & Expansion

We monitor detection quality, retrain as seasons, layouts and workwear change, and extend the same safety computer vision setup to new zones, shifts or sites once the first one is stable.

Start with one zone, not the whole site

A pilot on a handful of cameras tells you within weeks whether this works in your environment — before any commitment to a full deployment.

Request a Pilot

Warehouse operations team planning a camera-based safety monitoring pilot

Best Fit

Who Benefits from AI Vision Safety

Any site where people and moving equipment share space, and where cameras are already installed, is a candidate. The payback is fastest where an incident is expensive — in downtime, insurance or regulatory exposure.

Manufacturing

Production floors combine heavy machinery, strict PPE requirements and constant movement. Continuous monitoring catches the guard left open or the operator inside a machine zone at the moment it happens, not at the next audit. Workplace safety camera ai gives EHS teams a consistent standard across every shift, including the night one nobody observes.

Machine-zone entry and lockout compliance
Line-side PPE checks by area
Evidence trail for regulatory inspection

Warehousing and Logistics

Forklift and pedestrian conflict is the defining risk in a warehouse. Proximity tracking counts every close call and maps where they cluster, turning aisle layout from an opinion into a decision backed by numbers.

Forklift–pedestrian proximity alerts
Loading-bay and dock-door safety
Blocked-aisle and blocked-exit detection

Real Estate

Developers and property managers use the same camera network for contractor safety on active sites and for occupancy, access and incident control in finished buildings — one deployment covering both phases of an asset’s life.

Contractor PPE compliance on construction sites
Restricted-area and after-hours access control
Occupancy and common-area incident logging

Retail and Commercial

Stockrooms, delivery areas and public floors each carry different risks. The same ai vision safety platform handles slip-hazard detection front of house and equipment safety in the back, plus queue and footfall analytics as a by-product.

Spill and slip-hazard detection on the shop floor
Stockroom and delivery-area safety
Occupancy, queue and traffic analytics

What We Build

Use Cases We Build

Illustrative examples of the kind of AI vision safety systems Braintix deploys — not client case studies, just a look at the shape of the work.

Use case

AI Video Analytics

AI connects to existing cameras, detects events, runs analytics on people, queues, zones and incidents, and writes it all to a searchable log — no manual footage review.

Use case

PPE Compliance Monitoring

Continuous checks at gates, lines and loading bays. Violations are logged with evidence and trigger an immediate alert to a supervisor rather than waiting for the next audit.

Use case

Near-Miss & Proximity Mapping

Person-to-vehicle and person-to-machine distance is tracked continuously, building a heat map of where a site is genuinely dangerous — ranked by frequency, not by complaints.

Need Help? Start Here

FAQ

What is AI vision safety software?

AI vision safety software analyses live video from workplace cameras and automatically recognises unsafe situations — missing protective equipment, people in restricted zones, blocked exits, spills, and dangerous proximity between workers and vehicles. It runs continuously on the streams your cameras already produce, logs each detected event with a video clip and timestamp, and sends an alert to whoever needs to act. Its purpose is prevention: catching the condition while it can still be corrected, instead of documenting it afterwards.

Do I need to replace my existing cameras?

In almost all cases, no. Any camera producing an RTSP or IP stream can be used, and most systems installed in the last ten years qualify. During Discovery we assess resolution, frame rate and angle per camera and tell you which ones are usable as they are, which need repositioning, and — rarely — which cannot support reliable detection. New hardware is a recommendation only when the physics genuinely require it.

Does the system use facial recognition?

No, not by default. Safety detection works on anonymous person and object recognition — the model sees “a person without a hard hat in zone 3”, not an identity. Faces can be blurred automatically in stored footage. Identity-linked features are only ever built where you have a lawful basis and explicitly request them, and they remain a separate, opt-in module.

How is the data stored and who has access to it?

You choose. Processing can run entirely on an edge device inside your network, so no video leaves the site, or in your own private cloud tenancy. Retention periods, access roles and audit logging are configured to your policy, and event records can be kept without keeping the underlying footage. Braintix does not retain your video, and every access to stored evidence is logged.

What does implementation and onboarding look like?

Discovery and camera assessment take about a week. A pilot on a small group of cameras runs two to four weeks, tuned against your recorded footage until detection quality is acceptable. Full site rollout, alert routing and team training typically add another two to four weeks. Supervisors need roughly an hour of onboarding, because alerts arrive in tools they already use rather than in a new console.

Get in Touch

Contact Us

Tell us about your site and what you need to see. We will come back with a concrete first step, not a generic proposal.

Email us

hello@braintix.ai

Call us

+380 73 374 03 08

@braintix_ai

Our location

Ukraine — working with teams worldwide